scholarly journals Price Forecasting Accuracy of the OECD-FAO's Agricultural Outlook and the European Commission DG AGRI's Medium-Term Agricultural Outlook Report

2021 ◽  
Vol 13 (3) ◽  
pp. 77-87
Author(s):  
Jiří Pokorný ◽  
◽  
Pavel Froněk ◽  
Keyword(s):  
European Commission ◽  
Common Wheat ◽  
Price Forecasting ◽  
Medium Term ◽  
Forecasting Accuracy ◽  
Systematic Pattern ◽  
Beef Prices

The OECD-FAO's Agricultural Outlook and the European Commission DG AGRI's Medium-term agricultural outlook report provide price forecasts. Users of these forecasts may be interested in their accuracy. This paper measures the accuracy for values forecast for the following year. These are very accurate as regards the AO EU price of poultry, the EC outlook price of common wheat and feed barley, but not so accurate as regards the EC outlookon beef prices. In some cases, discrepancies between the forecasts follow a systematic pattern. The paper also discovers how the OECD-FAO's outlook projections for a common wheat world representative price are changing from year to year. Usually they are positively correlated, but there are certain exceptions where their correlation is significantly negative. This means that the price projections of some commodities may vary dramatically.

scholarly journals Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models

2015 ◽  
Vol 7 (5) ◽  
pp. 127-136 ◽  
Author(s):  
Yumurtaci Aydo mu Hacer ◽  
Ekinci Aykut ◽  
Erdal Halil ◽  
Erdal Hamit
Keyword(s):  
Crude Oil ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Forecasting Accuracy ◽  
Ensemble Models ◽  
Bagging Ensemble

Economic thought at the European Commission and the creation of EMU (1957-1991)

2011 ◽  
pp. 63-80
Author(s):  
Ivo Maes
Keyword(s):  
European Commission ◽  
Analytical Framework ◽  
Policy Coordination ◽  
Economic Thought ◽  
Monetary Integration ◽  
Structural Elements ◽  
Anglo Saxon ◽  
Medium Term ◽  
Monetary Cooperation ◽  
Stabilisation Policies

To understand macroeconomic and monetary thought at the European Commission, two elements are crucial: firstly, the Rome Treaty, as it determined the mandate of the Commission and, secondly, the economic ideas in the different countries of the European Community, as economic thought at the Commission was to a large extent a synthesis and compromise of the main schools of thought in the Community. Initially, economic thought at the Commission was mainly a fusion of French and German ideas, with a certain predominance of French ideas. Later, Anglo-Saxon ideas would gain ground. At the beginning of the 1980s, the Commission's analytical framework became basically medium-term oriented, with an important role for supply-side and structural elements and a more cautious approach towards discretionary stabilisation policies. This facilitated the process of European integration, in the monetary area too, as consensus on stabilityoriented policies was a crucial condition for EMU. Over the years, the Commission has taken its role as guardian of the Treaties and initiator of Community policies very seriously, not least in the monetary area. It has always advocated a strengthening of economic policy coordination and monetary cooperation. In this paper, we first focus on the different schools which have been shaping economic thought at the Commission. This is followed by an analysis of the Rome Treaty, especially the monetary dimension. Thereafter, we go into the EMU process and the initiatives of the Commission to further European monetary integration. We will consider three broad periods: the early decades, the 1970s, and the Maastricht process.

scholarly journals ANN based method for improving gold price forecasting accuracy through modified gradient descent methods

2020 ◽  
Vol 9 (1) ◽  
pp. 46
Author(s):  
Shilpa Verma ◽  
G. T. Thampi ◽  
Madhuri Rao
Keyword(s):  
Gradient Descent ◽  
Likelihood Function ◽  
Error Function ◽  
Complex Problem ◽  
Descent Method ◽  
Gold Price ◽  
Price Forecasting ◽  
Gradient Descent Method ◽  
Forecasting Accuracy ◽  
Median Error

Forecast of prices of financial assets including gold is of considerable importance for planning the economy. For centuries, people have been holding gold for many important reasons such as smoothening inflation fluctuations, protection from an economic crisis, sound investment etc.. Forecasting of gold prices is therefore an ever important exercise undertaken both by individuals and groups. Various local, global, political, psychological and economic factors make such a forecast a complex problem. Data analysts have been increasingly applying Artificial Intelligence (AI) techniques to make such forecasts. In the present work an inter comparison of gold price forecasting in Indian market is first done by employing a few classical Artificial Neural Network (ANN) techniques, namely Gradient Descent Method (GDM), Resilient Backpropagation method (RP), Scaled Conjugate Gradient method (SCG), Levenberg-Marquardt method (LM), Bayesian Regularization method (BR), One Step Secant method (OSS) and BFGS Quasi Newton method (BFG). Improvement in forecasting accuracy is achieved by proposing and developing a few modified GDM algorithms that incorporate different optimization functions by replacing the standard quadratic error function of classical GDM. Various optimization functions investigated in the present work are Mean median error function (MMD), Cauchy error function (CCY), Minkowski error function (MKW), Log cosh error function (LCH) and Negative logarithmic likelihood function (NLG). Modified algorithms incorporating these optimization functions are referred to here by GDM_MMD, GDM_CCY, GDM_KWK, GDM_LCH and GDM_NLG respectively. Gold price forecasting is then done by employing these algorithms and the results are analysed. The results of our study suggest that  the forecasting efficiency improves considerably on applying the modified methods proposed by us.

scholarly journals Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 147 ◽  
Author(s):  
Shenghua Xiong ◽  
Chunfeng Wang ◽  
Zhenming Fang ◽  
Dan Ma
Keyword(s):  
Optimization Algorithm ◽  
Carbon Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Variational Mode Decomposition ◽  
Forecasting Accuracy ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Relevance Vector Regression ◽  
Accuracy And Stability

The accurate and stable forecasting of carbon prices is vital for governors to make policies and essential for market participants to make investment decisions, which is important for promoting the development of carbon markets and reducing carbon emissions in China. However, it is challenging to improve the carbon price forecasting accuracy due to its non-linearity and non-stationary characteristics, especially in multi-step-ahead forecasting. In this paper, a hybrid multi-step-ahead forecasting model based on variational mode decomposition (VMD), fast multi-output relevance vector regression (FMRVR), and the multi-objective whale optimization algorithm (MOWOA) is proposed. VMD is employed to extract the primary mode for the carbon price. Then, FMRVR, which is used as the forecasting module, is built on the preprocessed data. To achieve high accuracy and stability, the MOWOA is utilized to optimize the kernel parameter and input the lag of the FMRVR. The proposed hybrid forecasting model is applied to carbon price series from three major regional carbon emission exchanges in China. Results show that the proposed VMD-FMRVR-MOWOA model achieves better performance compared to several other multi-output models in terms of forecasting accuracy and stability. The proposed model can be a potential and effective technique for multi-step-ahead carbon price forecasting in China’s three major regional emission exchanges.

Price forecasting using wavelet transform and LSE based mixed model in Australian electricity market

2008 ◽  
Vol 2 (4) ◽  
pp. 521-546 ◽  
Author(s):  
S.K. Aggarwal ◽  
L.M. Saini ◽  
Ashwani Kumar
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Electricity Markets ◽  
Electricity Market ◽  
Mixed Model ◽  
Price Forecasting ◽  
Forecasting Accuracy ◽  
Content Type ◽  
Price Series ◽  
Mlr Model

PurposePrice forecasting is essential for risk management in deregulated electricity markets. The purpose of this paper is to propose a hybrid technique using wavelet transform (WT) and multiple linear regression (MLR) to forecast price profile in electricity markets.Design/methodology/approachPrice series is highly volatile and non‐stationary in nature. In this work, initially complete price series has been decomposed into separate 48 half‐hourly series and then these series have been categorized into different segments for price forecasting. For some segments, WT based MLR has been applied and for the other segments, simple MLR model has been applied. The model is general in nature and has been implemented for one day‐ahead price forecasting in National Electricity Market (NEM) of Australia. Participants can use the technique practically, since it predicts price well before submission of bids.FindingsForecasting performance of the proposed WT and MLR based mixed model has been compared with the three other models, an analytical model, a MLR model and an artificial neural network (ANN) based model. The proposed model was found to be better. Performance evaluation for different wavelets was performed, and it has been observed that for improving forecasting accuracy using WT, Daubechies wavelet of order two gives the best performance.Originality/valueForecasting accuracy improvement of an established technique by incorporating time domain and wavelet domain variables of the same time series into one set has been implemented in this work. The paper also attempts to explain how non‐stationarity can be removed from a non‐stationary time series by applying WT after appropriate statistical investigation. Moreover, real time electricity markets are highly unpredictable and yet under investigated. The model has been applied to NEM for the same reason.

scholarly journals Estimation of Indonesian Beef Price Forecasting Model

2019 ◽  
Vol 2 (1) ◽  
pp. 46-55
Author(s):  
Rimadhita Tiara Putri ◽  
Ketut Sukiyono ◽  
Eko Sumartono
Keyword(s):  
Arima Model ◽  
Retail Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Moving Averages ◽  
Forecasting Models ◽  
Forecasting Method ◽  
Decomposition Models ◽  
Beef Prices ◽  
Single Exponential

The need for beef in Indonesia tends to increase along with fluctuated beef prices. The existence of price fluctuations will be a risk for producers and consumers. Therefore, price information is necessary, especially the future beef price and price forecasting is the answer to the need. The purpose of this study is to analyze and identify the best forecasting models for domestic and international beef prices. The data used is monthly retail price data for domestic and international beef from 2013:1-2017:12. Four models used in this study, namely decomposition models, ARIMA, moving averages, and Single Exponential Smoothing are applied. The best forecasting method for forecasting domestic and international beef prices is the ARIMA model based on the lowest values of MAD, MAPE, and MSD.

scholarly journals PRICE FORECASTING MECHANISMS OF MODERN ENERGY MARKETS

2020 ◽  
Author(s):  
Vasyl Gorbachuk ◽  
◽  
Andrij Syrku ◽  
Seit-Bekir Suleimanov ◽  
◽  
...  
Keyword(s):  
Energy Storage ◽  
Supply Chains ◽  
Power Plants ◽  
Energy Markets ◽  
Power Grids ◽  
Price Forecasting ◽  
Medium Term ◽  
Short Term ◽  
Power Sources

The trends of European energy markets depend on the forecasting of fundamental price based on the modeling approaches for short-term physical electricity markets, including day-ahead trade markets for energy power, intra-day trade markets for energy power, trade for balancing or reserving energy capacity. The typical hierarchy of modeling on modern market consists of the fundamental model of long-term planning to years ahead (where stochastic aggregation or disaggregation for price forecasting takes place), the model of medium-term planning to months ahead (where the stochastic modeling of semi aggregated hydro energy with generation of cuts, at prices given, takes place), and the model of short-term planning to weeks ahead (where the deterministic modeling of disaggregated hydro energy, at prices given, takes place). The model of long-term planning is a fundamental one in the sense of a detailed and adequate description of market, supply, demand, and network topology. The models of medium-term and short-term planning are typical ones for regional markets. Energy storage technologies have changed modern energy markets. If the traditional power grids have worked like ultimate just-in-time supply chains without stocks and with almost immediate delivery of good (electricity), then modernized power grids will create new opportunities for their optimization and operation. The new power grids will resemble common supply chains with stocks (in the form of large-scale batteries and other energy storage devices), supply uncertainty (from variable power sources such as wind and solar power plants), high customer service requirements (under deregulating of the electricity market and entering of new competitors to the market), the newest pricing schemes (due to the new communication infrastructure allowing information transmission for real time pricing). An energy storage system can be viewed as a system of stocks, where the product stored is the energy instead of a traditional good. Then a series of models of energy storage management is based on the fundamental theory of inventory optimization. On the other hand, energy storage systems usually have more room for decision: in addition to the decision to purchase a product (as in classic inventory models), there may be decisions about the quantity and the price of product sales.

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